Hi,
I really hope Quantiacs can provide more strategy examples, especially about strategies using technical indicators, mixing data sets, machine learning, deep learning, reinforcement learning,...
I also noticed that the documentation hasn't updated anything new for quite a while. Hopefully there will be many updates, providing more strategy examples and techniques to use.
Thank you. @support @Vyacheslav_B
Hi, I'm testing the new ticker filtering methods that Quantiacs provides, but I'm getting the error Cannot cast ufunc 'multiply' output from dtype('float64') to dtype('int64') with casting rule 'same_kind'
This is my code
# Import basic libraries.
import xarray as xr
import pandas as pd
import numpy as np
# Import Quantiacs libraries.
import qnt.data as qndata # load and manipulate data
import qnt.output as qnout # manage output
import qnt.backtester as qnbt # backtester
import qnt.stats as qnstats # statistical functions for analysis
import qnt.graph as qngraph # graphical tools
import qnt.ta as qnta # indicators library
import qnt.xr_talib as xr_talib # indicators library
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
def filter_volatility(data, rolling_window, top_assets, metric="std", ascending=True):
"""
Filter and rank assets based on volatility over a rolling window.
Args:
data (xarray.Dataset): The dataset containing asset data.
rolling_window (int): Window size for the rolling volatility computation.
top_assets (int): Number of top assets to select.
metric (str): Volatility metric to use ('std' for standard deviation).
ascending (bool): Rank order, True for lowest first.
"""
prices = data.sel(field='close')
daily_returns = prices.diff('time') / prices.shift(time=1)
rolling_volatility = calc_rolling_metric(daily_returns, rolling_window, metric)
volatility_ranks = rank_assets_by(data, rolling_volatility, top_assets, ascending)
return volatility_ranks
def calc_rolling_metric(condition, rolling_window, metric="std"):
"""
Compute a rolling metric (standard deviation or mean) over a specified window for a given condition.
Args:
condition (xarray.DataArray): Data over which the metric is computed.
rolling_window (int): Window size for the rolling computation.
metric (str): Type of metric to compute ('std' for standard deviation, 'mean' for average).
Raises:
ValueError: If an unsupported metric is specified.
"""
if metric == "std":
return condition.rolling({"time": rolling_window}).std()
elif metric == "mean":
return condition.rolling({"time": rolling_window}).mean()
else:
raise ValueError(f"Unsupported metric: {metric}")
def rank_assets_by(data, criterion, top_assets, ascending):
"""
Rank assets based on a specified criterion. Returns a DataArray where top ranked assets are marked with a '1'.
Args:
data (xarray.Dataset): The dataset containing asset data.
criterion (xarray.DataArray): The data based on which assets are ranked.
top_assets (int): Number of top assets to select.
ascending (bool): True for ascending order, False for descending order.
"""
volatility_ranks = xr.DataArray(
np.zeros_like(data.sel(field='close').values),
dims=['time', 'asset'],
coords={'time': data.coords['time'], 'asset': data.coords['asset']}
)
for time in criterion.coords['time'].values:
daily_vol = criterion.sel(time=time)
ranks = (daily_vol if ascending else -daily_vol).rank('asset')
top_assets_indices = ranks.where(ranks <= top_assets, drop=True).asset.values
volatility_ranks.loc[dict(time=time, asset=top_assets_indices)] = 1
return volatility_ranks.fillna(0)
close = data.sel(field="close")
sma_slow = qnta.sma(close, 200)
sma_fast = qnta.sma(close, 20)
weights = xr.where(sma_slow < sma_fast, 1, -1)
def filter_sharpe_ratio(data, weights, top_assets):
stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True)
sharpe_ratio = stats_per_asset.sel(field="sharpe_ratio")
return rank_assets_by(data, sharpe_ratio, top_assets, ascending=False)
asset_filter = filter_sharpe_ratio(data, weights, 150)
weights *= asset_filter
# Liquidity filter and clean
is_liquid = data.sel(field="is_liquid")
weights = weights * is_liquid
weights = qnout.clean(weights, data, "stocks_nasdaq100")
stats = qnstats.calc_stat(data, weights.sel(time=slice("2006-01-01", None)))
display(stats.to_pandas().tail())
performance = stats.to_pandas()["equity"]
qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")
weights = weights.sel(time=slice(date,None))
qnout.check(weights, data, "stocks_nasdaq100")
qnout.write(weights) # to participate in the competition
Besides I couldn't import the qnfilter library so I had to call the function directly from the strategy. Looking forward to your support. Thank you
@Vyacheslav_B Hi, I just tried both ml backtest and single backtest. This is the ml_backtest result
However, when adding the single cell backtest after ml_backtest, the result is Nan, so how can I submit the strategy according to the single backtest? Looking forward to your answer. Thank you.
@vyacheslav_b said in Acess previous weights:
If you use a state and a function that returns the prediction for one day, you will not get correct results with precheck.
Theoretically, you can specify the number of partitions as all available days. or you can return all predictions
I have not checked how the precheck works.
If it works in parallel, you will not see the correct result even more so.
State in strategy limits you. I recommend not using it.
Thank you so much @Vyacheslav_B.
I just tried applying the single pass you suggested but the results were nan. Looking forward to your help when you have time. thank you very much
@vyacheslav_b I tried your way but when prechecking there was an error: some dates are missed in the portfolio_history and the sharpness was very low.
I went to precheck result html file and this is the error result and sharpe is nan
We look forward to receiving your support. Thank you
@support @Vyacheslav_B
@multi_byte-wildebeest I also encountered the situation that some dates are missed in the portfolio_history when running precheck and sharpe which are very small compared to when working on strategy. Looking forward to your help. thank you very much
@vyacheslav_b Hi. I would like to ask how to switch from ml_backtest to single backtest to submit? And will this move lead to forward looking risks?
If so, is there no way to satisfy submission without exceeding time while eliminating forward looking? Looking forward to your answers and support. Thank you.
Hi,
I really hope Quantiacs can provide more strategy examples, especially about strategies using technical indicators, mixing data sets, machine learning, deep learning, reinforcement learning,...
I also noticed that the documentation hasn't updated anything new for quite a while. Hopefully there will be many updates, providing more strategy examples and techniques to use.
Thank you. @support @Vyacheslav_B
Hi, I'm testing the new ticker filtering methods that Quantiacs provides, but I'm getting the error Cannot cast ufunc 'multiply' output from dtype('float64') to dtype('int64') with casting rule 'same_kind'
This is my code
# Import basic libraries.
import xarray as xr
import pandas as pd
import numpy as np
# Import Quantiacs libraries.
import qnt.data as qndata # load and manipulate data
import qnt.output as qnout # manage output
import qnt.backtester as qnbt # backtester
import qnt.stats as qnstats # statistical functions for analysis
import qnt.graph as qngraph # graphical tools
import qnt.ta as qnta # indicators library
import qnt.xr_talib as xr_talib # indicators library
data = qndata.stocks.load_ndx_data(min_date="2005-06-01")
def filter_volatility(data, rolling_window, top_assets, metric="std", ascending=True):
"""
Filter and rank assets based on volatility over a rolling window.
Args:
data (xarray.Dataset): The dataset containing asset data.
rolling_window (int): Window size for the rolling volatility computation.
top_assets (int): Number of top assets to select.
metric (str): Volatility metric to use ('std' for standard deviation).
ascending (bool): Rank order, True for lowest first.
"""
prices = data.sel(field='close')
daily_returns = prices.diff('time') / prices.shift(time=1)
rolling_volatility = calc_rolling_metric(daily_returns, rolling_window, metric)
volatility_ranks = rank_assets_by(data, rolling_volatility, top_assets, ascending)
return volatility_ranks
def calc_rolling_metric(condition, rolling_window, metric="std"):
"""
Compute a rolling metric (standard deviation or mean) over a specified window for a given condition.
Args:
condition (xarray.DataArray): Data over which the metric is computed.
rolling_window (int): Window size for the rolling computation.
metric (str): Type of metric to compute ('std' for standard deviation, 'mean' for average).
Raises:
ValueError: If an unsupported metric is specified.
"""
if metric == "std":
return condition.rolling({"time": rolling_window}).std()
elif metric == "mean":
return condition.rolling({"time": rolling_window}).mean()
else:
raise ValueError(f"Unsupported metric: {metric}")
def rank_assets_by(data, criterion, top_assets, ascending):
"""
Rank assets based on a specified criterion. Returns a DataArray where top ranked assets are marked with a '1'.
Args:
data (xarray.Dataset): The dataset containing asset data.
criterion (xarray.DataArray): The data based on which assets are ranked.
top_assets (int): Number of top assets to select.
ascending (bool): True for ascending order, False for descending order.
"""
volatility_ranks = xr.DataArray(
np.zeros_like(data.sel(field='close').values),
dims=['time', 'asset'],
coords={'time': data.coords['time'], 'asset': data.coords['asset']}
)
for time in criterion.coords['time'].values:
daily_vol = criterion.sel(time=time)
ranks = (daily_vol if ascending else -daily_vol).rank('asset')
top_assets_indices = ranks.where(ranks <= top_assets, drop=True).asset.values
volatility_ranks.loc[dict(time=time, asset=top_assets_indices)] = 1
return volatility_ranks.fillna(0)
close = data.sel(field="close")
sma_slow = qnta.sma(close, 200)
sma_fast = qnta.sma(close, 20)
weights = xr.where(sma_slow < sma_fast, 1, -1)
def filter_sharpe_ratio(data, weights, top_assets):
stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True)
sharpe_ratio = stats_per_asset.sel(field="sharpe_ratio")
return rank_assets_by(data, sharpe_ratio, top_assets, ascending=False)
asset_filter = filter_sharpe_ratio(data, weights, 150)
weights *= asset_filter
# Liquidity filter and clean
is_liquid = data.sel(field="is_liquid")
weights = weights * is_liquid
weights = qnout.clean(weights, data, "stocks_nasdaq100")
stats = qnstats.calc_stat(data, weights.sel(time=slice("2006-01-01", None)))
display(stats.to_pandas().tail())
performance = stats.to_pandas()["equity"]
qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")
weights = weights.sel(time=slice(date,None))
qnout.check(weights, data, "stocks_nasdaq100")
qnout.write(weights) # to participate in the competition
Besides I couldn't import the qnfilter library so I had to call the function directly from the strategy. Looking forward to your support. Thank you